Automated method for integrated analysis of back end of the line yield, line resistance/capacitance and process performance

ABSTRACT

A method of electrical device manufacturing that includes measuring a first plurality of dimensions and electrical performance from back end of the line (BEOL) structures; and comparing the first plurality of dimensions with a second plurality of dimensions from a process assumption model to determine dimension variations by machine vision image processing. The method further includes providing a plurality of scenarios for process modifications by applying machine image learning to the dimension variations and electrical variations in the in line electrical measurements from the process assumption model. The method further includes receiving production dimension measurements and electrical measurements at a manufacturing prediction actuator. The at least one of the dimensions or electrical measurements received match one of the plurality of scenarios the manufacturing prediction actuator using the plurality of scenarios for process modifications effectuates a process change.

BACKGROUND Technical Field

The present invention generally relates to semiconductor manufacturing,and more particularly to process control techniques for semiconductormanufacturing processes.

Description of the Related Art

The semiconductor industry has been characterized by sophisticatedhigh-tech equipment, a high degree of factory automation, andultra-clean manufacturing facilities that cost billions of dollars incapital investment and maintenance expense. In order to achieveacceptable yield and device performance levels, very tight processspecifications must be achieved.

SUMMARY

In one embodiment, a method of automated back end of the line (BEOL)analysis of semiconductor device manufacturing is provided that mayinclude measuring a first plurality of dimensions from a back end of theline (BEOL) structure of an electrical device and electrical performanceof the BEOL structure; and comparing the first plurality of dimensionswith a second plurality of dimensions from a process assumption model todetermine dimension variations by machine vision image processing. Themethod can continue with providing a plurality of scenarios for processmodifications by applying machine image learning to the dimensionvariations and electrical variations in the in line electricalmeasurements from the process assumption model. The plurality ofscenarios for process modifications are responsive to dimension andelectrical variations in production BEOL structures. The method may alsoinclude receiving production dimension measurements and productionelectrical measurements from a production track at a manufacturingprediction actuator, wherein when at least one of the dimensions orelectrical measurements received match one of the plurality of scenariosthe manufacturing prediction actuator using the plurality of scenariosfor process modifications effectuates a process change to bringproduction of the BEOL within the process assumption model.

In another embodiment, the method includes a system for automated backend of the line (BEOL) analysis of semiconductor device manufacturing.In some embodiments, the system may include an optical measuring devicefor measuring a first plurality of dimensions from a back end of theline (BEOL) structure of an electrical device; and an in line electricalperformance measuring apparatus for measuring in line resistance of BEOLstructures. The system further includes a machine vision image processorfor comparing the first plurality of dimensions with a second pluralityof dimensions from a process assumption model to determine dimensionvariations, and a machine learning engine for applying machine learningto the dimension variations and electrical variations in the in lineelectrical measurements from the process assumption model. The pluralityof scenarios for process modifications are responsive to dimension andelectrical variations in production BEOL structures. The system canfurther include a manufacturing prediction actuator for receivingproduction dimension measurements and production electrical measurementsfrom a production track, wherein when at least one of the dimensions orelectrical measurements received match one of the plurality of scenariosthe manufacturing prediction actuator effectuates a process change tobring production of the BEOL within the process assumption model.

In yet another embodiment a computer program product is provided. Insome embodiments, the computer program produce includes a computerreadable storage medium comprising a computer readable program forelectrical device manufacturing. The computer readable program whenexecuted on a computer causes the computer to perform the steps ofcomparing a first plurality of dimensions measured from a back end ofthe line (BEOL structure with a second plurality of dimensions from aprocess assumption model to determine dimension variations by machinevision image processing; and providing a plurality of scenarios forprocess modifications by applying machine learning to the dimensionvariations and electrical variations in the in line electricalmeasurements from the process assumption model, wherein the plurality ofscenarios for process modifications are responsive to dimension andelectrical variations in production BEOL structures. The method furtherincludes receiving production dimension and electrical measurements at amanufacturing prediction actuator, wherein when at least one of thedimensions or electrical measurements received match one of theplurality of scenarios, the manufacturing prediction actuator using theplurality of scenarios for process modifications effectuates a processchange to bring production of the BEOL structures within the processassumption model. The computer readable storage medium may benon-transistory.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 is a block/flow diagram showing a system/method of an automatedmethod for integrated analysis of back end of the line (BEOL) structuresto determine whether the process flow for forming the BEOL structures iswithin the process window, in accordance with an embodiment of thepresent invention.

FIG. 2 is a flow diagram of an automated method for integrated analysisof back end of the line (BEOL) structures.

FIG. 3A is a side cross-sectional view of a plurality of metal lines ina back end of the line (BEOL) structure illustrating an image taken ofthe BEOL structure by automated transition electron microscope (TEM).

FIG. 3B is a side cross-sectional view of machine image processing ofthe image depicted in FIG. 3A that outputs a digital model withdimensions based on the TEM image depicted in FIG. 3A.

FIG. 3C is a side cross-sectional view illustrating the application of aprocess assumption to the digital model depicted in FIG. 3B for machinelearning and prediction in providing an automated method for integratedanalysis of BEOL structures to determine whether the process flow forforming the BEOL structures is within the process window.

FIG. 4A is a side cross-sectional view illustrating an image taken ofthe BEOL structure by automated TEM for detection of variations within achemical mechanical planarization (CMP) processes.

FIG. 4B is a side cross-sectional view of machine image processing ofthe image depicted in FIG. 4A that outputs a digital model withdimensions based on the TEM image depicted in FIG. 4A.

FIG. 4C is a side cross-sectional view illustrating the application of aprocess assumption to the digital model depicted in FIG. 4B for machinelearning and prediction in providing an automated method for integratedanalysis of BEOL structures to determine whether the process flow forforming the BEOL structures is within the process window.

FIG. 5A is a side cross-sectional view illustrating a plurality of metallines in a back end of the line (BEOL) structure illustrating an imagetaken of the BEOL structure by automated transition electron microscope(TEM) preparation and imaging for detection of variations within theprocess window for lithographic processing.

FIG. 5B is a side cross-sectional view of machine image processing ofthe image depicted in FIG. 5A that outputs a digital model withdimensions based on the TEM image depicted in FIG. 5A.

FIG. 5C is a side cross-sectional view illustrating the application of aprocess assumption to the digital model depicted in FIG. 5B for machinelearning and prediction in providing an automated method for integratedanalysis of BEOL structures to determine whether the process flow forforming the BEOL structures is within the process window.

FIG. 6A is a side cross-sectional view illustrating a plurality of metallines in a BEOL structure illustrating an image taken by automated TEMfor detection of variations within the process window for metallization.

FIG. 6B is a side cross-sectional view of machine image processing ofthe image depicted in FIG. 6A that outputs a digital model withdimensions based on the TEM image depicted in FIG. 6A.

FIG. 6C is a side cross-sectional view illustrating the application of aprocess assumption to the digital model depicted in FIG. 6B for machinelearning and prediction in providing an automated method for integratedanalysis of BEOL structures to determine whether the process flow forforming the BEOL structures is within the process window.

FIG. 7 is a block/flow diagram showing the components of a manufacturingprediction actuator that can be integrated with the system depicted inFIG. 1 and the method depicted in FIG. 2.

DETAILED DESCRIPTION

In semiconductor manufacturing rapid cost and effective analysis of backend of the line (BEOL) resistance and yield variation is needed.Currently, chip yield and performance parameters are correlated tostructural changes, metrology data (defects) and process parametersmanually. The analysis process is slow, inefficient and labor intensive.Manual intervention by engineers can occur at several steps. In someembodiments, the methods, systems and computer program products providedherein may provide a faster and more efficient method of back end of theline (BEOL) analysis of semiconductor device manufacturing usingautomated sample preparation, automated image processing, and machinelearning algorithms to correlate process parameters to back end of theline (BEOL) interconnect performance parameters, such as resistance andyield, changes in the geometry of metal lines and metal line relatedstructures, and other metrology data. Exemplary applications/uses towhich the present invention can be applied include, but are not limitedto process control for structures in integrated circuits, such as: metallines, interconnects, contacts, contact pads, isolation structures,interlevel dielectrics, intralevel dielectrics, as well as other backend of the line structures.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of the present invention may beassembler instructions, instruction-set-architecture (ISA) instructions,machine instructions, machine dependent instructions, microcode,firmware instructions, state-setting data, or either source code orobject code written in any combination of one or more programminglanguages, including an object oriented programming language such asSMALLTALK, C++ or the like, and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions may also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Referring now to the drawings, in which like numerals represent the sameor similar elements and initially to FIG. 1, an automated back end ofthe line (BEOL) analysis of semiconductor device manufacturing isprovided. In some embodiments, a method is provided for determining themost likely failure modes for back end of the line (BEOL) metal linestructures post electroplating and chemical mechanical planarization;determining the responsible sectors/tools/recipes; and applyingcorrective actions when applicable in real time.

The method begins with providing a plurality of wafers that have beenprocessed through front end of the line (FEOL) processing and back endof the line processing (BEOL). The wafers are analyzed to providehistorical data 10 that is used in machine learning as part of theautomated process flow that is described herein with reference to FIGS.1-7. The wafers may include a semiconductor substrate, such as asemiconductor on insulator (SOI) substrate or bulk substrate, that iscomposed of a semiconductor material, such as a type IV semiconductormaterial, e.g., silicon, silicon germanium and/or germanium, or a typeIII-V semiconductor material, e.g., gallium arsenide (GaAs). Thesemiconductor substrate may be processed using FEOL processing toinclude devices, such as transistors, e.g., field effect transistors(FETs). Following FEOL processing, the wafers are further processed withback end of the line (BEOL) processing, which includes the formation ofdielectric layers, interlevel dielectric layers, metal lines,interconnects, metal vias, contact pads, contacts and other structuresused for electrical communication. The wafers following FEOL and theBEOL processing provide the samples 10 depicted in FIG. 1.

A plurality of samples 10 may be used to provide data, i.e., historicaldata, for the processing of the samples 10 through a historical datagenerator 20. For example, the number of samples 10 that are used toprovide the historical data can be equal to 100 wafers. It is noted thatthis only represents one example of the number of wafers that are usedto generate the historical data, and is not intended to limit thepresent disclosure solely thereto. For example, the number of samples 10that are used to provide the historical data through the historical datagenerator 20 can include 25 wafers to 175 wafers. In some embodiments,the plurality of samples 10 that provide the historical data areselected during a point in manufacturing in which the samples 10 beingmanufactured are within the process assumptions, i.e., the dimensionsand electrical performance, that are the specification for the productbeing produced.

In some embodiments, the historical data generating generator 20includes at least one processor, i.e., hardware processor, that executesinstructions stored on a memory for at least one of measuring andstoring data for the geometry and/or electrical performance of astructural feature that in some embodiments is formed during back end ofthe line processing. For example, the structural feature, i.e., BEOLstructural feature, being measured and recorded for producing thehistorical data through the historical data generator 20 may includeoptical critical dimensions (OCD) 21, which are taken using an opticaldiffraction measurement, which can begin step 1 of the method depictedin FIG. 2. The optical critical dimension (OCD) 21 can be taken frommetal lines, and can include a value for a top critical dimension (TCD),a middle critical dimension (MCD) and a bottom critical dimension (BCD).The optical critical dimensions (OCD) can be measured for the widthdimension of the metal line, but in some examples height dimension ofthe line and the length of the metal line can also be considered.

It is noted that the structural detail being measured from the back endof the line (BEOL) structure is not limited to only metal lines, asother trench structures and other electrical communication structures,e.g., metal via structures. Further, the BEOL structure detail beingmeasured can be the pitch separating electrical communicationsstructures, e.g., pitch separating metal lines and/or vias, and the BEOLstructure being measured can also be portions of dielectric material,such as interlevel dielectrics that are separating electricalcommunication structures from one another.

Referring to FIG. 1, the historical data generator 20 also includes anin-line electrical property testing apparatus 22. The in-line electricalproperty testing apparatus 22 may be an in-line resistance test fordetermining the resistance of the metal lines, and/or vias, in the backend of the line (BEOL) structure, which is another process step that canbe performed as part of step 1 of FIG. 2. The in-line electricalproperty testing apparatus 22 is not limited to only testing lineresistance.

Referring to FIG. 1, in some embodiments, the historical data generator20 also includes top down scanning electron microscope (SEM) automatednano-scale imaging 23, and a cross-section transmission electronmicroscope (TEM) 24. These tests further define the dimensions taken fordetermining optical critical dimensions (OCD) 21 as measured using theoptical diffraction measurement, which can provide another process stepthat can be performed as part of step 1 of FIG. 2. Further, the crosssection and top down images correlate the images to dimensions, whichcan then be used with machine image processor 25 and machine learningdevice 26. In some embodiments, the methods, systems and computerprogram products that are described herein can include cross-section TEMand top down SEM images to obtain nano/micro-scale structural detailsfor the samples 10.

Referring to FIG. 1, using the machine image processor 25, the methods,systems and computer program products provided herein provide a fasterand more efficient method of back end of the line (BEOL) analysis ofsemiconductor device manufacturing. In some embodiments, in machineimage processing 25, after the image is acquired from the down scanningelectron microscope (SEM) automated nano-scale imaging 23, and thecross-section transmission electron microscope (TEM) 24, digital imageprocessing techniques are used to extract the information related to thecritical dimensions of the samples 10, and the system can make decisions(such as pass/fail) based on the extracted information. The machineimage processor 2 may include at least one processor and memoryincluding instructions to be executed by the processor for correlatingthe images, i.e., down scanning electron microscope (SEM) automatednano-scale imaging 23 and the cross-section transmission electronmicroscope (TEM) 24, to the OCD dimensions 21. Multiple stages ofprocessing can be used in a sequence that ends up as a desired result. Atypical sequence might start with tools such as filters which modify theimage, followed by extraction of objects, then extraction (e.g.measurements, reading of codes) of data from those objects, followed bycommunicating that data, or comparing it against target vales to createand communicate “pass/fail” results.

Machine vision image processing methods can includestitching/registration, which is the combining of adjacent 2D or 3Dimages; and filtering, e.g., morphological filtering). Machine visionimage process can also include thresholding, and pixel counting, i.e.,the device counts the number of light or dark pixels. Machine visionimage processing methods that can also be provided by the machine imageprocessor 25 may also include segmentation, which is the partitioning adigital image into multiple segments to simplify and/or change therepresentation of an image into something that is more meaningful andeasier to analyze. Machine vision image processing methods that can beprovided by the machine image processor 25 can also include edgedetection, which is the finding of the edges of objects. Machine visionimage processing methods that can be provided by the machine imageprocessor 25 can also include color analysis, which are used to identifyparts, products and items in an image using color, assess quality fromcolor, and isolate features using color. Machine vision image processingmethods that can be provided by the machine image processor 25 can alsoinclude blob discovery and manipulation, which are used to identifyparts, products and items in an image using color, assess quality fromcolor, and isolate features using color. Machine vision image processingmethods that can be provided by the machine image processor 25 can alsoinclude blob discovery and manipulation, which are used to identifyparts, products and items in an image using color, assess quality fromcolor, and isolate features using color. Machine vision image processingmethods that can be provided by the machine image processor 25 can alsopattern recognition including template matching, which includes finding,matching, and/or counting specific patterns. In yet another example, themachine vision image processing methods that can be provided by themachine image processor 25 can also include gauging/metrology, which isa measurement of object dimensions (e.g. in pixels, inches ormillimeters).

It is noted that the machine image processor 25 may employ any of theaforementioned methods, and can employ combinations of theaforementioned methods to perform step 2 of the method depicted in FIG.2. The machine image processor 25 using the above described methodsanalyze the structural details of the back end of the line (BEOL)structures and compare those structural details to a standard processassumption model. The “standard process assumption model” is thespecifications for which the device, i.e., device on the wafer, that thedevice is being manufactured to. For example, the standard processassumption model may dictate that the metal lines have a line heightspec of 60 nm with an allowable variation from the design spec of =/−5nm. Depending on whether the manufacturing process is operating withinits correct process window, the manufacturing process will or will notprovide wafers having back end of the line features meeting the standardprocess assumption model. The methods, systems and computer programproducts being described herein automate the changes needed to theprocess flow to keep the wafers being produced having back end of theline (BEOL) features within the standard process assumption model, aswell as electrical performance within the standard process assumptionmodel.

The machine image processor 25 matches the dimensions taken from thesamples 10 that are within the standard process assumption noting thosesamples, e.g., tagging those samples, as operating within a properprocess window, and matches the dimension from the samples 10 that areoutside the standard process assumption noting those samples, e.g.,tagging those samples, as not operating within a proper process window.The process conditions, e.g., etching, chemical mechanicalplanarization, deposition, photolithography, metallization and otherprocess conditions needed for manufacturing of semiconductor devicecomponents, such as back end of the line metallization, e.g., metallines, are known for each of the samples 10. This data can be referredto as the yield to nano/micro scale structural details obtained from themachine image processor 25.

Referring to FIG. 1, the box identified by reference number 16 is amachine learning engine. In some embodiments, the machine learningengine 16 uses artificial intelligence as it enables computers to getinto a mode of self-learning without being explicitly programmed. Themachine learning engine 16 may include at least one processor andmemory, the processor for executing instructions on the memory for thepurposes of producing a manufacturing prediction actuator 27. Morespecifically, in some embodiments, following the operations by themachine image processor 25, the machine learning engine 26 can correlatethe electrical property measurements from the in-line electricalproperty testing apparatus 21 and the yield to nano/micro scalestructural details obtained from the machine image processor 25, incombination with data from standard process control (SPC) and faultdetection classification systems, to specific variations within theprocess window for manufacturing the device. The standard processcontrol (SPC) may include systems that attribute variations inprocessing to known variations in the wafers being produced. The faultdetection classification system includes a lists of the types ofprocessing variations that produce certain types of variations in thesamples.

As noted above, each of the samples 10 is formed by a known processsequence having a number of parameters. The machine learning engine 26can correlate variations in the dimensions taken using optical criticaldimension (OCD) 21 matched to the images from the top down scanningelectron microscope (SEM) automated nano-scale imaging 23, and thecross-section transmission electron microscope (TEM) 24 from thedimensions from the standard process assumption model, as well asvariations in the measured electrical properties of the samples 10 fromthe from the in-line electrical property testing apparatus 21, and canattribute those variations to variations of the process window fordifferent processes, e.g., lithography, etching, deposition, chemicalmechanical planarization, etc., within the process sequence formanufacturing the samples 10.

The correlation of the aforementioned variations in dimensions andvariations in electrical performance in the samples to variations in theprocess window for manufacturing the samples using the machine learningengine 26 can include decision tree learning, associated rule learning,artificial neural networks, deep tree learning, inductive logicprogramming, support vector machines, clustering, bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, genetic algorithms, rule-basedmachine learning, learning classifier systems and combinations thereof.The machine learning engine 26 can provide one embodiment of step 3 ofFIG. 2.

Referring to FIG. 1, the machine learning engine 26 by correlating thevariations in the dimensions of the samples 10 to variations in theprocess windows of the various operations, e.g., lithography, etching,deposition, planarization, and combinations thereof, and correlatingtaken as well as variations in the measured electrical properties of thesamples 10 from the from the in-line electrical property testingapparatus 21 and employing at least one of the above mentioned forms ofmachine learning can provide predictive algorithm for the manufacturingprediction actuator 27 that can determine when variations in dimensionsand/or electrical properties measured in production samples of wafersfor BEOL structures necessitate changes to the process window for thevarious processes, e.g., lithography, etching, deposition, planarizationand combinations thereof, employed in producing production samples. Thepredictive algorithm uses correlations built from machine learning,i.e., the machine learning engine 26, to suggest a most likely failuremode and which tools/recipe/process parameters are responsible for thisfailure.

During high volume manufacturing these algorithms provide informationthat illustrate the likely cause for failure with corresponding proof,e.g., images/data, statistical confidence, correlation co-efficientbetween resistance and failing process parameters. In some embodiments,via automated systems in connectivity with apparatuses used for themanufacturing of samples/production samples including back end of theline (BEOL) structures, such as photolithography apparatuses, etchprocess apparatuses, deposition apparatus, and/or planarizationapparatus, the manufacturing prediction actuator 27 employing thepredictive algorithm can effectuate change in the aforementionedapparatuses to improve manufacturing performance. In one embodiment, thepredictive algorithm can determine whether the variations in dimensionsand/or electrical problems are the result of variations in the processwindow of an etch process, variations in the process window of aplanarization process and/or variations in the process window of alithographic process. It is noted, that examples of predictivealgorithms that are employed by the manufacturing prediction actuator 27are described in greater detail below with reference to FIGS. 3A-6C.

Referring to FIG. 1, in some embodiments, following using the historicaldata 20 to provide the manufacturing prediction actuator 27, themanufacturing predication actuator 27 can then be employed duringmanufacturing in a manufacturing track 30, in which in response tomeasurements of dimensions and electrical performance of productionsamples, the algorithms used in the manufacturing prediction actuator 27can provide information and make adjustments to the processes within themanufacturing track to ensure that the production samples conform to thestandard process assumption model. Additionally, data taken from thesample wafers, e.g., dimension data such as measurements of the opticalcritical dimension (OCD) 31 that is measured using optical diffractionmethods, and electrical performance measurements, such as in electricalproperty measurements taken using the in-line electrical propertytesting apparatus 31, can be used to retrain the manufacturingprediction actuator 27 through the machine learning engine 26. Themanufacturing prediction actuator 27 can receive inputs from theproduction track 30 to provide step 4 of FIG. 2.

Referring to FIG. 1, the manufacturing track 30 can include all of theapparatus 31 that are needed for the production of sample wafers andproduction wafers, i.e., referred to herein interchangeably as samples10. For example, the production apparatus 31 may include devices used inlithography. These can include spinners for the deposition ofphotoresist materials, light sources, mask aligners, mask writers, deepUV chambers and mask development benches. For example, the productionapparatus 31 may also include film deposition and growth apparatuses,such as physical vapor deposition (PVD) apparatuses, chemical vapordeposition apparatuses, thermal evaporation deposition apparatus andcombinations thereof. The production apparatus 31 may also includeannealing apparatus, such as furnace annealing apparatus and rapidthermal anneal (RTA) apparatus. The production apparatus 31 may alsoinclude etching apparatus, such as reactive ion etching apparatus,plasma etching apparatus, and induced coupled plasma etchers. Theproduction apparatus 31 may also include etching apparatus, such asreactive ion etching apparatus, plasma etching apparatus, inducedcoupled plasma etchers and pots, such as hot pots for wet chemicaletching. The production apparatus 31 can also include polishing,grinding and cleaving apparatus, such as chemical mechanicalplanarization (CMP) devices, dicing saws, grinders, and polishers. It isnoted that the production track 30 including the production apparatus 31can provide samples 10 for both production, and for producing thehistorical data.

FIG. 1 further depicts that the production track 30 also includesstatistical process control (SPC) charts for key process parameters forthe processes, such as lithography, etching, deposition, planarizationand combinations thereof, that are being executed by the productionapparatus 31. At least one production sample 10 produced by theproduction apparatus 31 of the production track 30 can be then selectedfor characterization to determine whether the production track 30 isoperating within acceptable process windows. For example, using anautomated process the at least one production sample 10 may be selected,and measurements can be taken of a dimension, such as a criticaldimension, of a back end of the line (BEOL) structure. The back end ofthe line (BEOL) structure being measured from the at least oneproduction sample 10 can be any BEOL structure, such as the examples ofBEOL structures that were described for producing the historical data.For example, the structural feature, i.e., BEOL structural feature,being measured from the at least one production sample 10 may include anoptical critical dimensions (OCD) 31, which are taken using an opticaldiffraction measurement. The optical critical dimension (OCD) 31 can betaken from metal lines, and can include a value for a top criticaldimension (TCD), a middle critical dimension (MCD) and a bottom criticaldimension (BCD). The optical critical dimensions (OCD) can be measuredfor the width dimension of the metal line, but in some examples heightdimension of the line and the length of the metal line can also beconsidered. The optical critical dimension (OCD) measurement 31 that isapplied to the production sample 10 is similar to the optical criticaldimension (OCD) measurement 21 that is described above with respect tothe historical data 20. Therefore, the optical critical dimension (OCD)measurement 21 for the historical data 20 can provide further detailsfor the optical critical dimension (OCD) measurement 31 that is appliedto the at least one production sample 10. It is further noted thatalthough the examples provided herein are directed to measurements toback end of the line structures, measurements at this stage of theprocess flow can also be directed to front end of the line (FEOL)structures.

Further, the at least one production sample 10 may be furthercharacterized by measuring the electrical performance of the sample,e.g., measuring the electrical performance of a back end of the linestructure, such as the in line resistance of a metal line. Theelectrical performance of the sample can be measured using an in-lineelectrical property testing apparatus 32. The in-line electricalproperty testing apparatus 32 may be an in-line resistance test fordetermining the resistance of the metal lines, and/or vias, in the backend of the line (BEOL) structure. The in-line electrical propertytesting apparatus 32 is not limited to only testing line resistance.Additionally, the in line electrical property testing apparatus 32 thatis used to measure the electrical properties of the at least oneproduction sample 10 is similar to the in line electrical propertytesting apparatus 22 that is used to measure the electrical propertiesof the samples 10 for the historical data 20.

Referring to FIG. 1, the dimensions taken from the at least one sample10 from the production track 30 from the optical critical dimension(OCD) measurement 31 and the electrical characterization taken from thean in-line electrical property testing apparatus 32 are then enteredinto the manufacturing prediction actuator 27, which uses the predictionalgorithms to determine whether the at least one sample 10 meets thestandard process assumption model, and if the at least one sample 10does not meet the specifications of the standard process assumptionmodel what changes need to be implemented to the process window of theproduction apparatus 31 of the production track 30 to provide productionsamples 10 meeting the standard process assumption model. Examples ofhow the manufacturing prediction actuator 27 employs predictionalgorithms to determine from measurements taken from production samples10 what changes need to be made in the production track 30 to keep theproduction samples 10 performing within the standard process assumptionmodel are described with more detail with reference to FIGS. 3A-7B. Themanufacturing prediction actuator 27 correlates performance parameterslike yield and resistance to the macro/nano scale structural details,such as trench dimensions, defects delamination, voids, and alsocorrelates those parameters to key process parameters, such as processparameters to be changed in the production apparatus 31 in theproduction track 30.

The output from the manufacturing prediction actuator 27 can be to anapparatus actuator 35 which can make the appropriate changes to any oneof the production apparatus 31 in the production track 30. For example,if the dimensions measured in the at least one sample 10 is interpretedas manufacturing prediction actuator 27 as being the result oflithography processes being operated outside of their process window,the manufacturing prediction actuator 27 can send instructions to theapparatus actuator 35. The actuator 35 can cause a change in the processconditions of the production apparatus 31 for the lithography window tochange the lithography process in a manner that brings the lithographyprocess within the appropriate process window to produce samples 10meeting the specifications according to the standard process assumptionmodel. This can provide one example of step 5 of FIG. 2.

FIG. 1 further illustrates how the using the data measured from the atleast one sample 10 following the production track 30 is used tore-train the model, e.g., the manufacturing prediction actuator 27including the prediction algorithm, because each data point measured canbe entered into the machine learning engine 26.

FIG. 3A depicts a plurality of metal lines in a back end of the line(BEOL) structure illustrating an image taken of the BEOL structure byautomated transition electron microscope (TEM). The image depicted inFIG. 3A illustrates one example of the output of the transmissionelectron microscope (TEM) 24 that can be used to produce images for themachine image processor 25 as part of the process flow for producing thepredictive algorithm used by the manufacturing prediction actuator 27,which is produced by the samples 10 of the historical data 20. Thedimensions are mapped from the OCD 21 measurements to the TEM image.FIG. 3B depicts machine image processing of the image depicted in FIG.3A that outputs a digital model with dimensions based on the TEM imagedepicted in FIG. 3A. FIG. 3B illustrates one example of the output ofthe machine image processor 25 for providing data to the machinelearning engine 26. The digital model includes accurate H1, H2dimensions based on the TEM 24, which included the dimensions measuredfrom the OCD 21. In this example, a first height dimension is a lineheight H1 and a second height dimension for an interlevel dielectriclayer H2 (also referred to as stud height) providing separation betweenstacked metal lines.

FIG. 3C illustrates the application of a process assumption model to thedigital model depicted in FIG. 3B for machine learning by the machinelearning engine 26 and prediction in providing an automated method forintegrated analysis of back end of the line (BEOL) structures todetermine whether the process flow for forming the BEOL structures iswithin the process window. In this example, the process assumption modelincludes a specification line height H3 for the metal lines and aspecification height H4 for the interlevel dielectric between the stacksof metal lines. The box with the dotted line identified by referencenumber 40 is an overlay of the specification for the metal line from theprocess assumption model. In some embodiments, variation of the processassumption model from the measured dimensions is taken from a referencepoint 41.

Referring to FIGS. 3A-3C, from the detected variation from the processassumption model, as well as any variation in the measured electricalperformance for the samples from the process assumption modelspecification for the electrical performance, the learning machineengine 26 produces a predictive algorithm for use by the manufacturingprediction actuator 27 to effectuate process changes in the apparatuses31 of the production track 30 to ensure that the back end of the line(BEOL) structures of the production samples 10 are within specification.

In one example, the predictive algorithm includes the followingconditions: if an etch issue is present, e.g., reactive ion etch (RIE)process is outside the process window, the stud height for the BEOLstructures will be too great, the line height may be on target, and theelectrical resistance is lower than the target value. One example ofthis scenario is depicted in FIGS. 3A-3C, in which the comparison of theprocess assumption dimensions and the actual dimensions illustrates theoverlay alignment (i.e., alignment in the x and y directions) was off bya minimal amount from the specification, e.g., 2 nm or less. In theexample depicted in FIGS. 3A-3C, the stud height, i.e., comparison of H2and H4, illustrates a significant difference on the order of 5 nm, i.e.,a difference between 37 nm for the process assumption and 32 nm for theactual dimension. This is one factor that can be indicative of a dryetch issue. Additionally, a significant difference, e.g., a differenceof 5 nm or greater, between the line height for the process assumptionand the actual dimension of the line height, e.g., comparison of H1 andH3, as illustrated in FIGS. 2A-2C is another factor that is indicativeof a dry etch issue. A line resistance that is less than the processassumption is also indicative of a etch issue.

In view of the dimensions for the BEOL structures depicted in FIGS.3A-3C, and in-line resistance testing indicating that the lineresistance for the metal lines is less than the process assumptionvalue, the manufacturing prediction actuator 27 sends instructions tothe apparatus actuator 35 that the etch sector, e.g., reactive ion etch(RIE) sector, is the likely origin of process deviation (line heightincrease=stud height decrease). For example, the manufacturingprediction actuator 27 can send instructions to the apparatus actuator35 to make automated adjustments, e.g., gas flow, etch time etch, to theetch apparatus 31 in the production track 30.

FIG. 4A illustrates a plurality of metal lines in a back end of the line(BEOL) structure illustrating an image taken of the BEOL structure byautomated transition electron microscope (TEM) preparation and imagingfor detection of variations within the process window for a firstembodiment of chemical mechanical planarization (CMP) processes. Theimage depicted in FIG. 4A illustrates another example of the output ofthe transmission electron microscope (TEM) 24 that can be used toproduce images for the machine image processor 25 as part of the processflow for producing the predictive algorithm used by the manufacturingprediction actuator 27, which is produced by the samples 10 of thehistorical data 20. The dimensions are mapped from the OCD 21 to the TEMimage. FIG. 4B depicts a machine image processing of the image depictedin FIG. 4A that outputs a digital model with dimensions based on the TEMimage depicted in FIG. 4A. FIG. 4B illustrates one example of the outputof the machine image processor 25 for providing data to the machinelearning engine 26. The digital model includes accurate H1, H2dimensions based on the TEM 24, which included the dimensions measuredfrom the OCD 21. In this example, a first height dimension is a lineheight H1 and a second height dimension for an interlevel dielectriclayer H2 (also referred to as stud height) providing separation betweenstacked metal lines. FIG. 4C illustrates the application of a processassumption to the digital model depicted in FIG. 4B. In this example,the process assumption model includes a specification line height H3 forthe metal lines and a specification height H4 for the interleveldielectric between the stacks of metal lines. The box with the dottedline identified by reference number 40 is an overlay of thespecification for the metal line from the process assumption model. Insome embodiments, variation of the process assumption model from themeasured dimensions is taken from a reference point 41.

Referring to FIGS. 4A-4C, from the detected variation from the processassumption model, as well as any variation in the measured electricalperformance for the samples from the process assumption modelspecification for the electrical performance, the learning machineengine 26 produces a predictive algorithm that detects issues withplanarization processes, e.g., whether the planarization process, suchas chemical mechanical planarization, is operating within a processwindow that can provide back end of the line (BEOL) structures meetingthe process assumption model specification.

In one example, the predictive algorithm includes the followingconditions: if a planarization issue is present, e.g., the chemicalmechanical planarization (CMP) process is outside the process window,the stud height for the BEOL structures will be too short, the lineheight may be on target, and the electrical resistance is higher thanthe target value. One example of this scenario is depicted in FIGS.3A-3C, in which the stud height, i.e., comparison of H2 and H4, is ontarget, but a significant difference, e.g., a difference of 10 nm orgreater, is present between the line height for the process assumptionand the actual dimension of the line height, e.g., comparison of H1 andH3. This scenario is indicative of a planarization issue in theproduction track 30. Further, a line resistance that is greater than theprocess assumption is also indicative of a planarization issue.

In view of the dimensions for the BEOL structures depicted in FIGS.4A-4C, and in-line resistance testing indicating that the lineresistance for the metal lines is less than the process assumptionvalue, the manufacturing prediction actuator 27 sends instructions tothe apparatus actuator 35 that the planarization sector, e.g., chemicalmechanical planarization (CMP) sector, is the likely origin of processdeviation. For example, the manufacturing prediction actuator 27 cansend instructions to the apparatus actuator 35 to make automatedadjustments, e.g., CMP removal rates, to the apparatus 31 in theproduction track 30.

FIG. 5A illustrates a plurality of metal lines in a back end of the line(BEOL) structure illustrating an image taken of the BEOL structure byautomated transition electron microscope (TEM) preparation and imagingfor detection of variations within the process window for lithographicprocessing. The dimensions are mapped from the OCD 21 to the TEM image.FIG. 5B illustrates machine image processing of the image depicted inFIG. 5A that outputs a digital model with dimensions based on the TEMimage. FIG. 5B illustrates one example of the output of the machineimage processor 25 for providing data to the machine learning engine 26.The digital model includes accurate H1, H2 dimensions based on the TEM24, which included the dimensions measured from the OCD 21. In thisexample, a first height dimension is a line height H1 and a secondheight dimension for an interlevel dielectric layer H2′, H2″ providingseparation between stacked metal lines, which is also referred to asstud height. FIG. 5C illustrates the application of a process assumptionto the digital model depicted in FIG. 5B for machine learning andprediction in providing an automated method for integrated analysis ofback end of the line (BEOL) structures to determine whether the processflow for forming the BEOL structures is within the process window. Inthis example, the process assumption model includes a specification lineheight H3 for the metal lines and a specification height H4′, H4″ forthe interlevel dielectric, i.e., stud height, between the stacks ofmetal lines. The box with the dotted line identified by reference number40 is an overlay of the specification for the metal line from theprocess assumption model.

Referring to FIGS. 5A-5C, from the detected variation from the processassumption model, as well as any variation in the measured electricalperformance for the samples from the process assumption modelspecification for the electrical performance, the learning machineengine 26 produces a predictive algorithm that detects issues with thelithography processes, e.g., whether the lithography process, such asmasking and/or exposure and/or developing, is operating within a processwindow that can provide back end of the line (BEOL) structures meetingthe process assumption model specification. In one example, thepredictive algorithm includes the following conditions: if a lithographyissue is present, e.g., the lithography process is outside the processwindow, the stud height and trench height will vary from line to line,but the overall electrical resistance can be similar to the processassumption model specification. One example of this scenario is depictedin FIGS. 5A-5C, in which both the line height H1, H2 and the stud heightH3, H4′, H4″ for the metal lines of the back end of the line (BEOL)structures varies from line to line. The high variation in the studheight and the metal line height, as depicted in FIGS. 5A-5C, incombination with a line resistance having a high variation line to line,but having an overall average with the process assumption modelspecification, is indicative of a variation in the lithography processesthat causes the production samples to be outside the specification ofthe process assumption model.

In view of the dimensions for the metal lines depicted in FIGS. 5A-5C,and in-line resistance testing indicating that the line resistance forthe metal lines is overall within the specification but having a highline to line variation, the manufacturing prediction actuator 27 sendsinstructions to the apparatus actuator 35 that the lithography sector,e.g., production apparatus 31 for lithography processes, is the likelyorigin of process deviation. For example, the manufacturing predictionactuator 27 can send instructions to the apparatus actuator 35 to makeautomated adjustments to the apparatus 31 for lithography in theproduction track 30 to account for pitch walking.

FIG. 6A depicts a plurality of metal lines in a back end of the line(BEOL) structure illustrating an image taken of the BEOL structure byautomated transition electron microscope (TEM) preparation and imagingfor detection of variations within the process window for metallization.FIG. 6B depicts a machine image processing of the image depicted in FIG.6A that outputs a digital model with dimensions based on the TEM imagedepicted in FIG. 6A. FIG. 6C illustrates the application of a processassumption to the digital model depicted in FIG. 6B for machine learningand prediction in providing an automated method for integrated analysisof back end of the line (BEOL) structures to determine whether theprocess flow for forming the BEOL structures is within the processwindow. In FIGS. 6A-6C, the line height measured from the productionsamples is identified by reference letter H1, and the line height fromthe process assumption model is identified by reference number H3. Thebox with the dotted line identified by reference number 40 is an overlayof the specification for the metal line from the process assumptionmodel.

Referring to FIGS. 6A-6C, from the detected variation from the processassumption model, as well as any variation in the measured electricalperformance for the samples from the process assumption modelspecification for the electrical performance, the learning machineengine 26 produces a predictive algorithm that detects issues with themetallization processes, e.g., whether the metallization process, e.g.,plating, physical vapor deposition or chemical vapor deposition, isoperating within a process window that can provide back end of the line(BEOL) structures meeting the process assumption model specification. Inone example, the predictive algorithm includes the following conditions:if a metallization issue is present, e.g., the metallization process isoutside the process window resulting in delamination, the stud heightand trench height can be on target, i.e., H1 is equal to H3, but thereis a contrast variation at the side walls on the left and right handside of the metal line in the trench. The contrast variations are theresult of voids 42 present in the middle of the line sidewalls. Further,the line resistance measured by the in line electrical property testingapparatus 22 of the metal lines is higher than the specification of theprocess assumption model. The aforementioned contrast variations andhigh line resistance is indicative of a metallization problem with theproduction track 30. In view of the sidewall contrast variations andhigh resistance of the metal lines, the manufacturing predictionactuator 27 sends instructions to the apparatus actuator 35 that themetallization sector, e.g., production apparatus 31 for metallographyprocesses, is the likely origin of process deviation.

FIG. 7 depicts one embodiment of the components of a manufacturingprediction actuator that can be integrated with the system depicted inFIG. 1. In FIG. 8, the manufacturing prediction actuator 27, the machineleaning engine 26 and the machine image processor 25 are depicted inelectrical communication with the system bus 105. FIG. 7 depicts oneexample of an exemplary processing system 100 to which the presentinvention may be applied. The processing system 100 includes at leastone processor (CPU) 102 operatively coupled to other components via asystem bus 105. A cache 106, a Read Only Memory (ROM) 108, a RandomAccess Memory (RAM) 110, an input/output (I/O) adapter 120, a soundadapter 130, a network adapter 140, a user interface adapter 150, and adisplay adapter 160, are operatively coupled to the system bus 105.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the I/O adapter 120. Thestorage devices 122 and 124 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 122 and 124 can be the same type ofstorage device or different types of storage devices. A speaker 132 isoperatively coupled to system bus 102 by the sound adapter 130. Atransceiver 142 is operatively coupled to system bus 102 by networkadapter 140. A display device 162 is operatively coupled to system bus102 by display adapter 160. A first user input device 152, a second userinput device 154, and a third user input device 156 are operativelycoupled to system bus 102 by user interface adapter 150. The first inputdevice 152 may be receive an image from the scanning electron microscope(SEM) providing the automated nano-scale imaging 23 and/or across-section transmission electron microscope (TEM) 24. The secondinput device 154 may be an input for the optical critical dimensions(OCD) 21, 31 are taken using an optical diffraction measurement as partof the historical data 20 track or the production track 30. The thirdinput device 156 may be an input for electrical performance measurementstaken from the in line electrical property testing apparatus 22 of thehistorical data track 20 and/or the in line electrical property testingapparatus 32 of the production track 30. The system depicted in FIG. 7may also include other input devices, such as any of a keyboard, amouse, a keypad, an image capture device, a motion sensing device, amicrophone, a device incorporating the functionality of at least two ofthe preceding devices, and so forth. Of course, other types of inputdevices can also be used, while maintaining the spirit of the presentinvention. The system may further include an output device 158 forproviding instructions to the apparatus actuator 35.

Having described preferred embodiments of an automated method forintegrated analysis of BEOL yield, line resistance/capacitance andprocess performance (which are intended to be illustrative and notlimiting), it is noted that modifications and variations can be made bypersons skilled in the art in light of the above teachings. It istherefore to be understood that changes may be made in the particularembodiments disclosed which are within the scope of the invention asoutlined by the appended claims. Having thus described aspects of theinvention, with the details and particularity required by the patentlaws, what is claimed and desired protected by Letters Patent is setforth in the appended claims.

What is claimed is:
 1. A method of electrical device manufacturingcomprising: measuring a first plurality of dimensions and electricalperformance from a back end of the line (BEOL) structure; comparing thefirst plurality of dimensions with a second plurality of dimensions froma process assumption model to determine dimension variations by machinevision image processing; providing a plurality of scenarios for processmodifications by applying machine learning to the dimension variationsand electrical variations in the in line electrical measurements fromthe process assumption model, wherein the plurality of scenarios forprocess modifications are responsive to dimension and electricalvariations in production BEOL structures; and receiving productiondimension and electrical measurements at a manufacturing predictionactuator, wherein when at least one of the dimensions or electricalmeasurements received match one of the plurality of scenarios, themanufacturing prediction actuator using the plurality of scenarios forprocess modifications effectuates a process change to bring productionof the BEOL structures within the process assumption model.
 2. Themethod of claim 1, wherein said measuring the first plurality ofdimensions from a back end of the line structure comprises opticaldiffraction measurements.
 3. The method of claim 1, further comprisingmapping the first plurality of dimensions taken from back end of theline (BEOL) structures from historical samples to a cross sectionalimage taken by transmission electron microscope (TEM).
 4. The method ofclaim 3, wherein the mapping comprises machine vision image processing.5. The method of claim 1, wherein the electrical performance of the BEOLstructure is in line resistance.
 6. The method of claim 1, wherein themachine learning is provided by a learning scheme selected from thegroup consisting of tree learning, associated rule learning, artificialneural networks, deep tree learning, inductive logic programming,support vector machines, clustering, bayesian networks, reinforcementlearning, representation learning, similarity and metric learning,sparse dictionary learning, genetic algorithms, rule-based machinelearning, learning classifier systems and combinations thereof.
 7. Themethod of claim 1, wherein the plurality of scenarios for processmodifications are responsive to dimension and electrical variations isan production apparatus adjustment that is selected from the groupconsisting of etch apparatus, process modifications for lithographyapparatus, process modifications for metallization apparatus, andcombinations thereof.
 8. The method of claim 1, wherein the BEOLstructure comprises a metal line.
 9. The method of claim 8, wherein thefirst plurality of dimensions from the BEOL structure comprises a metalline width, metal line height or a combination thereof.
 10. The methodof claim 8, wherein the first plurality of dimensions from the BEOLstructure comprises a stud height, top variation in the metal lineheight, side variation in the metal line height, or a combinationthereof.